ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Evaluation of COVID-19 vaccine breakthrough infections among immunocompromised patients fully vaccinated with BNT162b2
This article has 11 authors:Reviewed by ScreenIT
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Mapping Molecular Gene Signatures Among Respiratory Viruses Based on Large-Scale and Genome-wide Transcriptomics Analysis
This article has 3 authors:Reviewed by ScreenIT
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Nucleocapsid mutations in SARS-CoV-2 augment replication and pathogenesis
This article has 19 authors:Reviewed by ScreenIT
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Agile design and development of a high throughput cobas SARS-CoV-2 RT-PCR diagnostic test
This article has 12 authors:Reviewed by ScreenIT
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The Usefulness of Antigen Testing in Predicting Contagiousness in COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Mechanism of optimal time-course COVID-19 vaccine prioritization based on non-Markovian steady-state prediction
This article has 3 authors:Reviewed by ScreenIT
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Genome Recombination between the Delta and Alpha Variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)
This article has 8 authors:Reviewed by ScreenIT
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Evaluation of real-life use of Point-Of-Care Rapid Antigen TEsting for SARS-CoV-2 in schools (EPOCRATES)
This article has 16 authors:Reviewed by ScreenIT
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Public Perception of COVID-19 Vaccines on Twitter in the United States
This article has 8 authors:Reviewed by ScreenIT
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Risk of SARS-CoV-2 infection among front-line healthcare workers in Northeast Brazil: a respondent-driven sampling approach
This article has 25 authors:Reviewed by ScreenIT